How are policies evaluated?
Best insight from top research papers
Policies are evaluated using various methods. One approach is to learn a single value function that can evaluate and improve multiple policies represented by deep neural networks (NN) . This method involves learning a small set of "probing states" and a mapping from actions produced in these states to the policy's return. By changing actions in these probing states, the policy can be improved following the gradient of the value function's predictions. Another method involves adaptively weighting observations to control the variance of doubly robust (DR) estimators used for policy evaluation . This improved estimator allows for the formation of confidence intervals and hypothesis testing. Additionally, a policy evaluation device can compare the control policy of a group to be evaluated with other control policies and generate evaluation information based on the comparison result .
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| Papers (5) | Insight |
|---|---|
| The paper does not provide specific details on how policies are evaluated. | |
Open access•Posted Content | The paper evaluates policies by using historical data collected from contextual bandits and improving the doubly robust estimator by adaptively weighting observations to control variance. |
04 Jul 2022 | The paper proposes a method to learn a single value function that can evaluate and improve any policy represented by a deep neural network. |
3 Citations | The paper proposes a method to learn a single value function that can evaluate and improve any policy represented by a deep neural network. |
14 Aug 2021 24 Citations | The paper proposes an improved doubly robust estimator for policy evaluation by adaptively weighting observations to control variance. |
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